{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-24T07:13:43.844176Z",
     "start_time": "2025-04-24T07:13:43.826190Z"
    }
   },
   "source": [
    "def calculate_average_complex(numbers):\n",
    "    total = 0\n",
    "    count = 0\n",
    "    for number in numbers:\n",
    "        total += number\n",
    "        count += 1\n",
    "    if count == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return total / count\n",
    "\n",
    "def calculate_average_simple(numbers):\n",
    "    return sum(numbers) / len(numbers) if numbers else 0\n",
    "\n",
    "# 测试两个函数\n",
    "test_data = [1, 2, 3, 4, 5]\n",
    "result_complex = calculate_average_complex(test_data)\n",
    "result_simple = calculate_average_simple(test_data)\n",
    "\n",
    "result_complex, result_simple"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.0, 3.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:13:43.874994Z",
     "start_time": "2025-04-24T07:13:43.855975Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def calculate_average(data):\n",
    "    if not data:\n",
    "        return 0\n",
    "    return sum(data) / len(data)\n",
    "\n",
    "# 测试不同类型的数据\n",
    "integers = [1, 2, 3, 4, 5]\n",
    "floats = [1.0, 2.0, 3.0, 4.0, 5.0]\n",
    "mixed = [1, 2.0, 3, 4.0, 5]\n",
    "\n",
    "average_integers = calculate_average(integers)\n",
    "average_floats = calculate_average(floats)\n",
    "average_mixed = calculate_average(mixed)\n",
    "\n",
    "average_integers, average_floats, average_mixed"
   ],
   "id": "89f776828d139cbb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.0, 3.0, 3.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:13:44.998249Z",
     "start_time": "2025-04-24T07:13:43.897039Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 生成数据\n",
    "np.random.seed(0)\n",
    "X = np.random.rand(100, 1) * 10\n",
    "y = 3 * X.squeeze() + 5 + np.random.randn(100) * 2\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# 创建并训练模型\n",
    "degree = 10  # 使用高次多项式来模拟过拟合\n",
    "poly_features = PolynomialFeatures(degree=degree)\n",
    "X_train_poly = poly_features.fit_transform(X_train)\n",
    "X_test_poly = poly_features.transform(X_test)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_poly, y_train)\n",
    "\n",
    "# 预测\n",
    "y_train_pred = model.predict(X_train_poly)\n",
    "y_test_pred = model.predict(X_test_poly)\n",
    "\n",
    "# 计算经验误差和测试误差\n",
    "train_error = mean_squared_error(y_train, y_train_pred)\n",
    "test_error = mean_squared_error(y_test, y_test_pred)\n",
    "\n",
    "# 绘图\n",
    "plt.scatter(X, y, color='blue', label='实际数据')\n",
    "plt.plot(X_train, y_train_pred, color='red', label='训练数据拟合')\n",
    "plt.plot(X_test, y_test_pred, color='green', label='测试数据预测')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('y')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "(train_error, test_error)\n"
   ],
   "id": "171ce07dc1123f32",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 38469 (\\N{CJK UNIFIED IDEOGRAPH-9645}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25454 (\\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25311 (\\N{CJK UNIFIED IDEOGRAPH-62DF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 21512 (\\N{CJK UNIFIED IDEOGRAPH-5408}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(3.644025357963051, 4.621121580982397)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
